Overview

Dataset statistics

Number of variables15
Number of observations86512
Missing cells323414
Missing cells (%)24.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 MiB
Average record size in memory120.0 B

Variable types

Categorical6
Numeric9

Alerts

country has a high cardinality: 223 distinct valuesHigh cardinality
iso_code has a high cardinality: 223 distinct valuesHigh cardinality
date has a high cardinality: 483 distinct valuesHigh cardinality
vaccines has a high cardinality: 84 distinct valuesHigh cardinality
source_name has a high cardinality: 81 distinct valuesHigh cardinality
source_website has a high cardinality: 119 distinct valuesHigh cardinality
total_vaccinations is highly overall correlated with people_vaccinated and 5 other fieldsHigh correlation
people_vaccinated is highly overall correlated with total_vaccinations and 3 other fieldsHigh correlation
people_fully_vaccinated is highly overall correlated with total_vaccinations and 6 other fieldsHigh correlation
daily_vaccinations_raw is highly overall correlated with total_vaccinations and 3 other fieldsHigh correlation
daily_vaccinations is highly overall correlated with total_vaccinations and 3 other fieldsHigh correlation
total_vaccinations_per_hundred is highly overall correlated with total_vaccinations and 3 other fieldsHigh correlation
people_vaccinated_per_hundred is highly overall correlated with total_vaccinations and 3 other fieldsHigh correlation
people_fully_vaccinated_per_hundred is highly overall correlated with people_fully_vaccinated and 2 other fieldsHigh correlation
vaccines is highly overall correlated with source_nameHigh correlation
source_name is highly overall correlated with vaccinesHigh correlation
total_vaccinations has 42905 (49.6%) missing valuesMissing
people_vaccinated has 45218 (52.3%) missing valuesMissing
people_fully_vaccinated has 47710 (55.1%) missing valuesMissing
daily_vaccinations_raw has 51150 (59.1%) missing valuesMissing
total_vaccinations_per_hundred has 42905 (49.6%) missing valuesMissing
people_vaccinated_per_hundred has 45218 (52.3%) missing valuesMissing
people_fully_vaccinated_per_hundred has 47710 (55.1%) missing valuesMissing

Reproduction

Analysis started2023-03-14 19:15:51.332338
Analysis finished2023-03-14 19:16:22.413538
Duration31.08 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

country
Categorical

Distinct223
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size676.0 KiB
Norway
 
482
Latvia
 
480
Denmark
 
476
United States
 
471
Russia
 
470
Other values (218)
84133 

Length

Max length32
Median length25
Mean length8.7223507
Min length4

Characters and Unicode

Total characters754588
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Norway 482
 
0.6%
Latvia 480
 
0.6%
Denmark 476
 
0.6%
United States 471
 
0.5%
Russia 470
 
0.5%
Canada 470
 
0.5%
China 470
 
0.5%
Israel 466
 
0.5%
Qatar 463
 
0.5%
Liechtenstein 463
 
0.5%
Other values (213) 81801
94.6%

Length

2023-03-15T00:46:22.601585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 3213
 
2.9%
islands 2336
 
2.1%
united 1363
 
1.2%
saint 1237
 
1.1%
new 1193
 
1.1%
south 1157
 
1.0%
guinea 1108
 
1.0%
republic 1067
 
1.0%
ireland 899
 
0.8%
of 774
 
0.7%
Other values (250) 97814
87.2%

Most occurring characters

ValueCountFrequency (%)
a 112929
15.0%
n 63394
 
8.4%
i 61229
 
8.1%
e 52076
 
6.9%
r 43279
 
5.7%
o 40746
 
5.4%
t 29627
 
3.9%
l 28889
 
3.8%
u 27920
 
3.7%
s 27341
 
3.6%
Other values (46) 267158
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 619785
82.1%
Uppercase Letter 107797
 
14.3%
Space Separator 25649
 
3.4%
Other Punctuation 392
 
0.1%
Open Punctuation 323
 
< 0.1%
Close Punctuation 323
 
< 0.1%
Dash Punctuation 319
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 112929
18.2%
n 63394
10.2%
i 61229
9.9%
e 52076
 
8.4%
r 43279
 
7.0%
o 40746
 
6.6%
t 29627
 
4.8%
l 28889
 
4.7%
u 27920
 
4.5%
s 27341
 
4.4%
Other values (16) 132355
21.4%
Uppercase Letter
ValueCountFrequency (%)
S 12811
11.9%
C 9358
 
8.7%
M 8659
 
8.0%
B 8057
 
7.5%
I 7123
 
6.6%
A 6915
 
6.4%
G 6678
 
6.2%
N 6193
 
5.7%
T 5251
 
4.9%
L 4870
 
4.5%
Other values (15) 31882
29.6%
Space Separator
ValueCountFrequency (%)
25649
100.0%
Other Punctuation
ValueCountFrequency (%)
' 392
100.0%
Open Punctuation
ValueCountFrequency (%)
( 323
100.0%
Close Punctuation
ValueCountFrequency (%)
) 323
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 727582
96.4%
Common 27006
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 112929
15.5%
n 63394
 
8.7%
i 61229
 
8.4%
e 52076
 
7.2%
r 43279
 
5.9%
o 40746
 
5.6%
t 29627
 
4.1%
l 28889
 
4.0%
u 27920
 
3.8%
s 27341
 
3.8%
Other values (41) 240152
33.0%
Common
ValueCountFrequency (%)
25649
95.0%
' 392
 
1.5%
( 323
 
1.2%
) 323
 
1.2%
- 319
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 754588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 112929
15.0%
n 63394
 
8.4%
i 61229
 
8.1%
e 52076
 
6.9%
r 43279
 
5.7%
o 40746
 
5.4%
t 29627
 
3.9%
l 28889
 
3.8%
u 27920
 
3.7%
s 27341
 
3.6%
Other values (46) 267158
35.4%

iso_code
Categorical

Distinct223
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size676.0 KiB
NOR
 
482
LVA
 
480
DNK
 
476
USA
 
471
RUS
 
470
Other values (218)
84133 

Length

Max length8
Median length3
Mean length3.1419456
Min length3

Characters and Unicode

Total characters271816
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG

Common Values

ValueCountFrequency (%)
NOR 482
 
0.6%
LVA 480
 
0.6%
DNK 476
 
0.6%
USA 471
 
0.5%
RUS 470
 
0.5%
CAN 470
 
0.5%
CHN 470
 
0.5%
ISR 466
 
0.5%
QAT 463
 
0.5%
LIE 463
 
0.5%
Other values (213) 81801
94.6%

Length

2023-03-15T00:46:22.864470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nor 482
 
0.6%
lva 480
 
0.6%
dnk 476
 
0.6%
usa 471
 
0.5%
rus 470
 
0.5%
can 470
 
0.5%
chn 470
 
0.5%
isr 466
 
0.5%
qat 463
 
0.5%
lie 463
 
0.5%
Other values (213) 81801
94.6%

Most occurring characters

ValueCountFrequency (%)
R 19400
 
7.1%
A 19361
 
7.1%
N 19297
 
7.1%
M 16111
 
5.9%
S 15934
 
5.9%
L 14639
 
5.4%
G 13811
 
5.1%
B 13396
 
4.9%
I 12982
 
4.8%
C 12740
 
4.7%
Other values (17) 114145
42.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 269360
99.1%
Connector Punctuation 2456
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 19400
 
7.2%
A 19361
 
7.2%
N 19297
 
7.2%
M 16111
 
6.0%
S 15934
 
5.9%
L 14639
 
5.4%
G 13811
 
5.1%
B 13396
 
5.0%
I 12982
 
4.8%
C 12740
 
4.7%
Other values (16) 111689
41.5%
Connector Punctuation
ValueCountFrequency (%)
_ 2456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 269360
99.1%
Common 2456
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 19400
 
7.2%
A 19361
 
7.2%
N 19297
 
7.2%
M 16111
 
6.0%
S 15934
 
5.9%
L 14639
 
5.4%
G 13811
 
5.1%
B 13396
 
5.0%
I 12982
 
4.8%
C 12740
 
4.7%
Other values (16) 111689
41.5%
Common
ValueCountFrequency (%)
_ 2456
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 19400
 
7.1%
A 19361
 
7.1%
N 19297
 
7.1%
M 16111
 
5.9%
S 15934
 
5.9%
L 14639
 
5.4%
G 13811
 
5.1%
B 13396
 
4.9%
I 12982
 
4.8%
C 12740
 
4.7%
Other values (17) 114145
42.0%

date
Categorical

Distinct483
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size676.0 KiB
2021-08-19
 
220
2021-08-17
 
220
2021-09-01
 
220
2021-08-31
 
220
2021-08-30
 
220
Other values (478)
85412 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters865120
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2021-02-22
2nd row2021-02-23
3rd row2021-02-24
4th row2021-02-25
5th row2021-02-26

Common Values

ValueCountFrequency (%)
2021-08-19 220
 
0.3%
2021-08-17 220
 
0.3%
2021-09-01 220
 
0.3%
2021-08-31 220
 
0.3%
2021-08-30 220
 
0.3%
2021-08-29 220
 
0.3%
2021-08-28 220
 
0.3%
2021-08-27 220
 
0.3%
2021-08-26 220
 
0.3%
2021-08-25 220
 
0.3%
Other values (473) 84312
97.5%

Length

2023-03-15T00:46:23.088487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-08-19 220
 
0.3%
2021-08-21 220
 
0.3%
2021-08-17 220
 
0.3%
2021-08-15 220
 
0.3%
2021-08-14 220
 
0.3%
2021-08-13 220
 
0.3%
2021-08-08 220
 
0.3%
2021-08-11 220
 
0.3%
2021-08-09 220
 
0.3%
2021-08-12 220
 
0.3%
Other values (473) 84312
97.5%

Most occurring characters

ValueCountFrequency (%)
2 243442
28.1%
0 193470
22.4%
- 173024
20.0%
1 142748
16.5%
3 22572
 
2.6%
8 15399
 
1.8%
7 15344
 
1.8%
6 15021
 
1.7%
5 14976
 
1.7%
9 14751
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 692096
80.0%
Dash Punctuation 173024
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 243442
35.2%
0 193470
28.0%
1 142748
20.6%
3 22572
 
3.3%
8 15399
 
2.2%
7 15344
 
2.2%
6 15021
 
2.2%
5 14976
 
2.2%
9 14751
 
2.1%
4 14373
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 173024
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 865120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 243442
28.1%
0 193470
22.4%
- 173024
20.0%
1 142748
16.5%
3 22572
 
2.6%
8 15399
 
1.8%
7 15344
 
1.8%
6 15021
 
1.7%
5 14976
 
1.7%
9 14751
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 865120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 243442
28.1%
0 193470
22.4%
- 173024
20.0%
1 142748
16.5%
3 22572
 
2.6%
8 15399
 
1.8%
7 15344
 
1.8%
6 15021
 
1.7%
5 14976
 
1.7%
9 14751
 
1.7%

total_vaccinations
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42828
Distinct (%)98.2%
Missing42905
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean45929645
Minimum0
Maximum3.263129 × 109
Zeros121
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size676.0 KiB
2023-03-15T00:46:23.357563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29750
Q1526410
median3590096
Q317012304
95-th percentile1.3804278 × 108
Maximum3.263129 × 109
Range3.263129 × 109
Interquartile range (IQR)16485894

Descriptive statistics

Standard deviation2.2460036 × 108
Coefficient of variation (CV)4.8900958
Kurtosis108.64256
Mean45929645
Median Absolute Deviation (MAD)3521244
Skewness9.8451401
Sum2.002854 × 1012
Variance5.0445322 × 1016
MonotonicityNot monotonic
2023-03-15T00:46:23.656772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 121
 
0.1%
39224 17
 
< 0.1%
70336 13
 
< 0.1%
70229 13
 
< 0.1%
43159 11
 
< 0.1%
10167 10
 
< 0.1%
8649 8
 
< 0.1%
6 7
 
< 0.1%
38589 6
 
< 0.1%
63011 6
 
< 0.1%
Other values (42818) 43395
50.2%
(Missing) 42905
49.6%
ValueCountFrequency (%)
0 121
0.1%
1 4
 
< 0.1%
2 5
 
< 0.1%
3 4
 
< 0.1%
4 3
 
< 0.1%
5 4
 
< 0.1%
6 7
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
3263129000 1
< 0.1%
3259042000 1
< 0.1%
3254984000 1
< 0.1%
3251412000 1
< 0.1%
3247624000 1
< 0.1%
3243599000 1
< 0.1%
3239170000 1
< 0.1%
3234601000 1
< 0.1%
3230367000 1
< 0.1%
3226334000 1
< 0.1%

people_vaccinated
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40194
Distinct (%)97.3%
Missing45218
Missing (%)52.3%
Infinite0
Infinite (%)0.0%
Mean17705078
Minimum0
Maximum1.275541 × 109
Zeros112
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size676.0 KiB
2023-03-15T00:46:23.978605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21576.85
Q1349464.25
median2187310.5
Q39152519.8
95-th percentile59177864
Maximum1.275541 × 109
Range1.275541 × 109
Interquartile range (IQR)8803055.5

Descriptive statistics

Standard deviation70787312
Coefficient of variation (CV)3.9981361
Kurtosis129.08684
Mean17705078
Median Absolute Deviation (MAD)2126537.5
Skewness10.384116
Sum7.3111348 × 1011
Variance5.0108435 × 1015
MonotonicityNot monotonic
2023-03-15T00:46:24.306533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
0.1%
5823245 28
 
< 0.1%
2113080 13
 
< 0.1%
26708 11
 
< 0.1%
26716 11
 
< 0.1%
10167 10
 
< 0.1%
24773 10
 
< 0.1%
486242 10
 
< 0.1%
928107 8
 
< 0.1%
8649 8
 
< 0.1%
Other values (40184) 41073
47.5%
(Missing) 45218
52.3%
ValueCountFrequency (%)
0 112
0.1%
1 4
 
< 0.1%
2 5
 
< 0.1%
3 4
 
< 0.1%
4 3
 
< 0.1%
5 4
 
< 0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
1275541000 1
< 0.1%
1274734000 1
< 0.1%
1273811000 1
< 0.1%
1273470000 1
< 0.1%
1272537000 1
< 0.1%
1269302000 1
< 0.1%
1268180000 1
< 0.1%
1266426000 1
< 0.1%
1266070000 1
< 0.1%
1265034000 1
< 0.1%

people_fully_vaccinated
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37425
Distinct (%)96.5%
Missing47710
Missing (%)55.1%
Infinite0
Infinite (%)0.0%
Mean14138300
Minimum1
Maximum1.240777 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size676.0 KiB
2023-03-15T00:46:24.623408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9883.45
Q1243962.25
median1722140.5
Q37559869.5
95-th percentile52111963
Maximum1.240777 × 109
Range1.240777 × 109
Interquartile range (IQR)7315907.2

Descriptive statistics

Standard deviation57139202
Coefficient of variation (CV)4.0414479
Kurtosis193.89413
Mean14138300
Median Absolute Deviation (MAD)1695386.5
Skewness12.306464
Sum5.4859431 × 1011
Variance3.2648884 × 1015
MonotonicityNot monotonic
2023-03-15T00:46:24.964565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 72
 
0.1%
3 37
 
< 0.1%
5 30
 
< 0.1%
2 27
 
< 0.1%
1179 27
 
< 0.1%
19019 20
 
< 0.1%
4 17
 
< 0.1%
6 14
 
< 0.1%
341 14
 
< 0.1%
26354 13
 
< 0.1%
Other values (37415) 38531
44.5%
(Missing) 47710
55.1%
ValueCountFrequency (%)
1 72
0.1%
2 27
 
< 0.1%
3 37
< 0.1%
4 17
 
< 0.1%
5 30
< 0.1%
6 14
 
< 0.1%
7 6
 
< 0.1%
8 5
 
< 0.1%
9 12
 
< 0.1%
10 12
 
< 0.1%
ValueCountFrequency (%)
1240777000 1
< 0.1%
1240413000 1
< 0.1%
1239706000 1
< 0.1%
1239570000 1
< 0.1%
1239171000 1
< 0.1%
1234540000 1
< 0.1%
1232543000 1
< 0.1%
1228340000 1
< 0.1%
1227387000 1
< 0.1%
1224450000 1
< 0.1%

daily_vaccinations_raw
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27692
Distinct (%)78.3%
Missing51150
Missing (%)59.1%
Infinite0
Infinite (%)0.0%
Mean270599.58
Minimum0
Maximum24741000
Zeros381
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size676.0 KiB
2023-03-15T00:46:25.286935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile222
Q14668
median25309
Q3123492.5
95-th percentile913195.8
Maximum24741000
Range24741000
Interquartile range (IQR)118824.5

Descriptive statistics

Standard deviation1212426.6
Coefficient of variation (CV)4.4805192
Kurtosis130.51895
Mean270599.58
Median Absolute Deviation (MAD)24315
Skewness10.262703
Sum9.5689423 × 109
Variance1.4699783 × 1012
MonotonicityNot monotonic
2023-03-15T00:46:25.618426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 381
 
0.4%
1 67
 
0.1%
4 26
 
< 0.1%
2 22
 
< 0.1%
5 22
 
< 0.1%
3 20
 
< 0.1%
17 18
 
< 0.1%
1208 18
 
< 0.1%
19 17
 
< 0.1%
7 17
 
< 0.1%
Other values (27682) 34754
40.2%
(Missing) 51150
59.1%
ValueCountFrequency (%)
0 381
0.4%
1 67
 
0.1%
2 22
 
< 0.1%
3 20
 
< 0.1%
4 26
 
< 0.1%
5 22
 
< 0.1%
6 10
 
< 0.1%
7 17
 
< 0.1%
8 12
 
< 0.1%
9 14
 
< 0.1%
ValueCountFrequency (%)
24741000 1
< 0.1%
24119000 1
< 0.1%
23605000 1
< 0.1%
23162000 1
< 0.1%
22918000 1
< 0.1%
22296000 1
< 0.1%
22039000 1
< 0.1%
21502000 1
< 0.1%
21425000 1
< 0.1%
21240000 1
< 0.1%

daily_vaccinations
Real number (ℝ)

Distinct40516
Distinct (%)47.0%
Missing299
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean131305.49
Minimum0
Maximum22424286
Zeros432
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size676.0 KiB
2023-03-15T00:46:25.944371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51
Q1900
median7343
Q344098
95-th percentile458403.8
Maximum22424286
Range22424286
Interquartile range (IQR)43198

Descriptive statistics

Standard deviation768238.77
Coefficient of variation (CV)5.8507744
Kurtosis301.45352
Mean131305.49
Median Absolute Deviation (MAD)7222
Skewness15.400852
Sum1.132024 × 1010
Variance5.9019081 × 1011
MonotonicityNot monotonic
2023-03-15T00:46:26.249884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 432
 
0.5%
2 395
 
0.5%
14 238
 
0.3%
196 187
 
0.2%
41369 182
 
0.2%
22369 177
 
0.2%
17 162
 
0.2%
1 157
 
0.2%
114 136
 
0.2%
608 130
 
0.2%
Other values (40506) 84017
97.1%
(Missing) 299
 
0.3%
ValueCountFrequency (%)
0 432
0.5%
1 157
 
0.2%
2 395
0.5%
3 89
 
0.1%
4 129
 
0.1%
5 69
 
0.1%
6 91
 
0.1%
7 97
 
0.1%
8 57
 
0.1%
9 40
 
< 0.1%
ValueCountFrequency (%)
22424286 1
< 0.1%
22366286 1
< 0.1%
22105857 1
< 0.1%
21998714 1
< 0.1%
21993000 1
< 0.1%
21935429 1
< 0.1%
21536000 1
< 0.1%
21253286 1
< 0.1%
21124714 1
< 0.1%
20801429 1
< 0.1%

total_vaccinations_per_hundred
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17881
Distinct (%)41.0%
Missing42905
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean80.188543
Minimum0
Maximum345.37
Zeros254
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size676.0 KiB
2023-03-15T00:46:26.569773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.76
Q116.05
median67.52
Q3132.735
95-th percentile204.19
Maximum345.37
Range345.37
Interquartile range (IQR)116.685

Descriptive statistics

Standard deviation67.913577
Coefficient of variation (CV)0.84692369
Kurtosis-0.58787677
Mean80.188543
Median Absolute Deviation (MAD)56.63
Skewness0.58326715
Sum3496781.8
Variance4612.2539
MonotonicityNot monotonic
2023-03-15T00:46:26.881768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 254
 
0.3%
0.01 102
 
0.1%
0.02 77
 
0.1%
0.07 55
 
0.1%
0.05 54
 
0.1%
0.06 53
 
0.1%
0.03 53
 
0.1%
0.04 50
 
0.1%
0.09 50
 
0.1%
0.1 45
 
0.1%
Other values (17871) 42814
49.5%
(Missing) 42905
49.6%
ValueCountFrequency (%)
0 254
0.3%
0.01 102
0.1%
0.02 77
 
0.1%
0.03 53
 
0.1%
0.04 50
 
0.1%
0.05 54
 
0.1%
0.06 53
 
0.1%
0.07 55
 
0.1%
0.08 35
 
< 0.1%
0.09 50
 
0.1%
ValueCountFrequency (%)
345.37 1
< 0.1%
343.85 1
< 0.1%
343.06 1
< 0.1%
342.15 1
< 0.1%
341.34 1
< 0.1%
340.67 1
< 0.1%
340.09 1
< 0.1%
339.62 1
< 0.1%
338.97 1
< 0.1%
337.54 1
< 0.1%

people_vaccinated_per_hundred
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9078
Distinct (%)22.0%
Missing45218
Missing (%)52.3%
Infinite0
Infinite (%)0.0%
Mean40.927317
Minimum0
Maximum124.76
Zeros243
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size676.0 KiB
2023-03-15T00:46:27.205093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.67
Q111.37
median41.435
Q367.91
95-th percentile83.02
Maximum124.76
Range124.76
Interquartile range (IQR)56.54

Descriptive statistics

Standard deviation29.290759
Coefficient of variation (CV)0.71567746
Kurtosis-1.311707
Mean40.927317
Median Absolute Deviation (MAD)28.115
Skewness0.067131506
Sum1690052.6
Variance857.94854
MonotonicityNot monotonic
2023-03-15T00:46:27.515676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 243
 
0.3%
0.01 106
 
0.1%
0.02 74
 
0.1%
0.07 67
 
0.1%
3.5 62
 
0.1%
0.05 53
 
0.1%
0.03 52
 
0.1%
0.06 49
 
0.1%
0.04 49
 
0.1%
0.09 48
 
0.1%
Other values (9068) 40491
46.8%
(Missing) 45218
52.3%
ValueCountFrequency (%)
0 243
0.3%
0.01 106
0.1%
0.02 74
 
0.1%
0.03 52
 
0.1%
0.04 49
 
0.1%
0.05 53
 
0.1%
0.06 49
 
0.1%
0.07 67
 
0.1%
0.08 33
 
< 0.1%
0.09 48
 
0.1%
ValueCountFrequency (%)
124.76 1
< 0.1%
124.75 1
< 0.1%
124.74 2
< 0.1%
124.7 1
< 0.1%
124.69 1
< 0.1%
124.66 2
< 0.1%
124.65 1
< 0.1%
124.6 1
< 0.1%
124.57 1
< 0.1%
124.37 1
< 0.1%

people_fully_vaccinated_per_hundred
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8772
Distinct (%)22.6%
Missing47710
Missing (%)55.1%
Infinite0
Infinite (%)0.0%
Mean35.523243
Minimum0
Maximum122.37
Zeros598
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size676.0 KiB
2023-03-15T00:46:27.847332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23
Q17.02
median31.75
Q362.08
95-th percentile78.87
Maximum122.37
Range122.37
Interquartile range (IQR)55.06

Descriptive statistics

Standard deviation28.376252
Coefficient of variation (CV)0.79880803
Kurtosis-1.2102774
Mean35.523243
Median Absolute Deviation (MAD)26.92
Skewness0.27785228
Sum1378372.9
Variance805.21167
MonotonicityNot monotonic
2023-03-15T00:46:28.165550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 598
 
0.7%
0.01 186
 
0.2%
0.02 115
 
0.1%
0.04 97
 
0.1%
0.08 92
 
0.1%
0.03 89
 
0.1%
0.07 68
 
0.1%
0.06 68
 
0.1%
0.05 62
 
0.1%
0.29 53
 
0.1%
Other values (8762) 37374
43.2%
(Missing) 47710
55.1%
ValueCountFrequency (%)
0 598
0.7%
0.01 186
 
0.2%
0.02 115
 
0.1%
0.03 89
 
0.1%
0.04 97
 
0.1%
0.05 62
 
0.1%
0.06 68
 
0.1%
0.07 68
 
0.1%
0.08 92
 
0.1%
0.09 49
 
0.1%
ValueCountFrequency (%)
122.37 1
< 0.1%
122.3 1
< 0.1%
122.06 1
< 0.1%
122.05 1
< 0.1%
122.01 1
< 0.1%
121.99 1
< 0.1%
121.67 1
< 0.1%
121.6 1
< 0.1%
121.59 1
< 0.1%
121.57 1
< 0.1%
Distinct12405
Distinct (%)14.4%
Missing299
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean3257.0492
Minimum0
Maximum117497
Zeros495
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size676.0 KiB
2023-03-15T00:46:28.482739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61
Q1636
median2050
Q34682
95-th percentile9940.4
Maximum117497
Range117497
Interquartile range (IQR)4046

Descriptive statistics

Standard deviation3934.3124
Coefficient of variation (CV)1.2079377
Kurtosis68.136795
Mean3257.0492
Median Absolute Deviation (MAD)1674
Skewness4.8388261
Sum2.8079998 × 108
Variance15478814
MonotonicityNot monotonic
2023-03-15T00:46:28.777172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 495
 
0.6%
184 225
 
0.3%
501 192
 
0.2%
607 188
 
0.2%
6762 186
 
0.2%
22 165
 
0.2%
176 163
 
0.2%
4 158
 
0.2%
7412 148
 
0.2%
43 146
 
0.2%
Other values (12395) 84147
97.3%
(Missing) 299
 
0.3%
ValueCountFrequency (%)
0 495
0.6%
1 71
 
0.1%
2 92
 
0.1%
3 112
 
0.1%
4 158
 
0.2%
5 144
 
0.2%
6 58
 
0.1%
7 72
 
0.1%
8 79
 
0.1%
9 77
 
0.1%
ValueCountFrequency (%)
117497 1
< 0.1%
117410 1
< 0.1%
110205 1
< 0.1%
109282 1
< 0.1%
101235 1
< 0.1%
92926 1
< 0.1%
83086 1
< 0.1%
78604 1
< 0.1%
73854 1
< 0.1%
72868 1
< 0.1%

vaccines
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct84
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size676.0 KiB
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech
7608 
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech
6263 
Oxford/AstraZeneca
6022 
Oxford/AstraZeneca, Pfizer/BioNTech
 
4629
Johnson&Johnson, Moderna, Novavax, Oxford/AstraZeneca, Pfizer/BioNTech
 
3564
Other values (79)
58426 

Length

Max length122
Median length90
Mean length53.587595
Min length7

Characters and Unicode

Total characters4635970
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing
2nd rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing
3rd rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing
4th rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing
5th rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing

Common Values

ValueCountFrequency (%)
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech 7608
 
8.8%
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech 6263
 
7.2%
Oxford/AstraZeneca 6022
 
7.0%
Oxford/AstraZeneca, Pfizer/BioNTech 4629
 
5.4%
Johnson&Johnson, Moderna, Novavax, Oxford/AstraZeneca, Pfizer/BioNTech 3564
 
4.1%
Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing 2484
 
2.9%
Moderna, Pfizer/BioNTech 2309
 
2.7%
Pfizer/BioNTech 2271
 
2.6%
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V 2041
 
2.4%
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing 2030
 
2.3%
Other values (74) 47291
54.7%

Length

2023-03-15T00:46:29.117689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oxford/astrazeneca 71331
21.1%
pfizer/biontech 64193
19.0%
moderna 37417
11.1%
johnson&johnson 35949
10.6%
sinopharm/beijing 34638
10.3%
sputnik 26176
 
7.7%
v 23060
 
6.8%
sinovac 19892
 
5.9%
novavax 4935
 
1.5%
covaxin 3435
 
1.0%
Other values (19) 16769
 
5.0%

Most occurring characters

ValueCountFrequency (%)
o 388067
 
8.4%
n 383018
 
8.3%
e 346528
 
7.5%
i 290775
 
6.3%
r 284022
 
6.1%
a 264567
 
5.7%
251283
 
5.4%
, 223542
 
4.8%
h 175683
 
3.8%
/ 171081
 
3.7%
Other values (42) 1857404
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3191493
68.8%
Uppercase Letter 755536
 
16.3%
Other Punctuation 430572
 
9.3%
Space Separator 251283
 
5.4%
Decimal Number 7086
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 388067
12.2%
n 383018
12.0%
e 346528
10.9%
i 290775
9.1%
r 284022
8.9%
a 264567
8.3%
h 175683
 
5.5%
c 157130
 
4.9%
s 143964
 
4.5%
f 135524
 
4.2%
Other values (13) 622215
19.5%
Uppercase Letter
ValueCountFrequency (%)
B 99246
13.1%
S 86960
11.5%
A 74069
9.8%
Z 72339
9.6%
J 71898
9.5%
O 71746
9.5%
N 69128
9.1%
P 64928
8.6%
T 64634
8.6%
M 37791
 
5.0%
Other values (12) 42797
5.7%
Other Punctuation
ValueCountFrequency (%)
, 223542
51.9%
/ 171081
39.7%
& 35949
 
8.3%
Decimal Number
ValueCountFrequency (%)
0 3543
50.0%
2 2535
35.8%
1 1008
 
14.2%
Space Separator
ValueCountFrequency (%)
251283
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3947029
85.1%
Common 688941
 
14.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 388067
 
9.8%
n 383018
 
9.7%
e 346528
 
8.8%
i 290775
 
7.4%
r 284022
 
7.2%
a 264567
 
6.7%
h 175683
 
4.5%
c 157130
 
4.0%
s 143964
 
3.6%
f 135524
 
3.4%
Other values (35) 1377751
34.9%
Common
ValueCountFrequency (%)
251283
36.5%
, 223542
32.4%
/ 171081
24.8%
& 35949
 
5.2%
0 3543
 
0.5%
2 2535
 
0.4%
1 1008
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4635970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 388067
 
8.4%
n 383018
 
8.3%
e 346528
 
7.5%
i 290775
 
6.3%
r 284022
 
6.1%
a 264567
 
5.7%
251283
 
5.4%
, 223542
 
4.8%
h 175683
 
3.8%
/ 171081
 
3.7%
Other values (42) 1857404
40.1%

source_name
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct81
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size676.0 KiB
World Health Organization
26822 
Ministry of Health
15027 
SPC Public Health Division
4300 
Pan American Health Organization
 
3075
Africa Centres for Disease Control and Prevention
 
2780
Other values (76)
34508 

Length

Max length70
Median length63
Mean length27.46612
Min length9

Characters and Unicode

Total characters2376149
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWorld Health Organization
2nd rowWorld Health Organization
3rd rowWorld Health Organization
4th rowWorld Health Organization
5th rowWorld Health Organization

Common Values

ValueCountFrequency (%)
World Health Organization 26822
31.0%
Ministry of Health 15027
17.4%
SPC Public Health Division 4300
 
5.0%
Pan American Health Organization 3075
 
3.6%
Africa Centres for Disease Control and Prevention 2780
 
3.2%
Government of the United Kingdom 2215
 
2.6%
Federal Office of Public Health 926
 
1.1%
Ministry of Public Health 871
 
1.0%
Norwegian Institute of Public Health 482
 
0.6%
National Health Service 480
 
0.6%
Other values (71) 29534
34.1%

Length

2023-03-15T00:46:29.449899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
health 60591
18.6%
of 36925
 
11.4%
organization 29897
 
9.2%
world 26822
 
8.3%
ministry 18434
 
5.7%
government 14035
 
4.3%
public 8758
 
2.7%
via 6469
 
2.0%
for 6190
 
1.9%
and 6125
 
1.9%
Other values (143) 110728
34.1%

Most occurring characters

ValueCountFrequency (%)
238462
 
10.0%
a 201508
 
8.5%
i 187171
 
7.9%
n 181727
 
7.6%
t 176777
 
7.4%
e 173900
 
7.3%
o 169322
 
7.1%
r 149893
 
6.3%
l 118589
 
5.0%
h 69227
 
2.9%
Other values (47) 709573
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1846579
77.7%
Uppercase Letter 272491
 
11.5%
Space Separator 238462
 
10.0%
Other Punctuation 9406
 
0.4%
Decimal Number 5787
 
0.2%
Dash Punctuation 3424
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 201508
10.9%
i 187171
10.1%
n 181727
9.8%
t 176777
9.6%
e 173900
9.4%
o 169322
9.2%
r 149893
8.1%
l 118589
 
6.4%
h 69227
 
3.7%
s 56987
 
3.1%
Other values (15) 361478
19.6%
Uppercase Letter
ValueCountFrequency (%)
H 62469
22.9%
O 33448
12.3%
W 27273
10.0%
M 23023
 
8.4%
P 21032
 
7.7%
C 19456
 
7.1%
G 17458
 
6.4%
S 13668
 
5.0%
D 12620
 
4.6%
A 9121
 
3.3%
Other values (13) 32923
12.1%
Other Punctuation
ValueCountFrequency (%)
. 5220
55.5%
/ 2060
 
21.9%
, 889
 
9.5%
' 825
 
8.8%
: 412
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 3112
53.8%
9 2675
46.2%
Space Separator
ValueCountFrequency (%)
238462
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3424
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2119070
89.2%
Common 257079
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 201508
 
9.5%
i 187171
 
8.8%
n 181727
 
8.6%
t 176777
 
8.3%
e 173900
 
8.2%
o 169322
 
8.0%
r 149893
 
7.1%
l 118589
 
5.6%
h 69227
 
3.3%
H 62469
 
2.9%
Other values (38) 628487
29.7%
Common
ValueCountFrequency (%)
238462
92.8%
. 5220
 
2.0%
- 3424
 
1.3%
1 3112
 
1.2%
9 2675
 
1.0%
/ 2060
 
0.8%
, 889
 
0.3%
' 825
 
0.3%
: 412
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2376149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
238462
 
10.0%
a 201508
 
8.5%
i 187171
 
7.9%
n 181727
 
7.6%
t 176777
 
7.4%
e 173900
 
7.3%
o 169322
 
7.1%
r 149893
 
6.3%
l 118589
 
5.0%
h 69227
 
2.9%
Other values (47) 709573
29.9%

source_website
Categorical

Distinct119
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size676.0 KiB
https://covid19.who.int/
25951 
https://ais.paho.org/imm/IM_DosisAdmin-Vacunacion.asp
 
4677
https://stats.pacificdata.org/vis?tm=covid&pg=0&df[ds]=SPC2&df[id]=DF_COVID_VACCINATION&df[ag]=SPC&df[vs]=1.0
 
4382
https://africacdc.org/covid-19-vaccination/
 
2239
https://coronavirus.data.gov.uk/details/vaccinations
 
2215
Other values (114)
47048 

Length

Max length248
Median length132
Mean length50.277071
Min length16

Characters and Unicode

Total characters4349570
Distinct characters74
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://covid19.who.int/
2nd rowhttps://covid19.who.int/
3rd rowhttps://covid19.who.int/
4th rowhttps://covid19.who.int/
5th rowhttps://covid19.who.int/

Common Values

ValueCountFrequency (%)
https://covid19.who.int/ 25951
30.0%
https://ais.paho.org/imm/IM_DosisAdmin-Vacunacion.asp 4677
 
5.4%
https://stats.pacificdata.org/vis?tm=covid&pg=0&df[ds]=SPC2&df[id]=DF_COVID_VACCINATION&df[ag]=SPC&df[vs]=1.0 4382
 
5.1%
https://africacdc.org/covid-19-vaccination/ 2239
 
2.6%
https://coronavirus.data.gov.uk/details/vaccinations 2215
 
2.6%
https://github.com/folkehelseinstituttet/surveillance_data 482
 
0.6%
https://data.gov.lv/dati/eng/dataset/covid19-vakcinacijas 480
 
0.6%
https://covid19.ssi.dk/overvagningsdata/download-fil-med-vaccinationsdata 476
 
0.6%
https://data.cdc.gov/Vaccinations/COVID-19-Vaccination-Trends-in-the-United-States-N/rh2h-3yt2 471
 
0.5%
https://covid19tracker.ca/vaccinationtracker.html 470
 
0.5%
Other values (109) 44669
51.6%

Length

2023-03-15T00:46:30.083714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://covid19.who.int 25951
30.0%
https://ais.paho.org/imm/im_dosisadmin-vacunacion.asp 4677
 
5.4%
https://stats.pacificdata.org/vis?tm=covid&pg=0&df[ds]=spc2&df[id]=df_covid_vaccination&df[ag]=spc&df[vs]=1.0 4382
 
5.1%
https://africacdc.org/covid-19-vaccination 2239
 
2.6%
https://coronavirus.data.gov.uk/details/vaccinations 2215
 
2.6%
https://github.com/folkehelseinstituttet/surveillance_data 482
 
0.6%
https://data.gov.lv/dati/eng/dataset/covid19-vakcinacijas 480
 
0.6%
https://covid19.ssi.dk/overvagningsdata/download-fil-med-vaccinationsdata 476
 
0.6%
https://data.cdc.gov/vaccinations/covid-19-vaccination-trends-in-the-united-states-n/rh2h-3yt2 471
 
0.5%
https://covid19tracker.ca/vaccinationtracker.html 470
 
0.5%
Other values (109) 44669
51.6%

Most occurring characters

ValueCountFrequency (%)
t 346616
 
8.0%
/ 339364
 
7.8%
i 257257
 
5.9%
o 245368
 
5.6%
s 241797
 
5.6%
a 239086
 
5.5%
c 197005
 
4.5%
. 195192
 
4.5%
d 160752
 
3.7%
h 154074
 
3.5%
Other values (64) 1973059
45.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2943428
67.7%
Other Punctuation 652199
 
15.0%
Decimal Number 297345
 
6.8%
Uppercase Letter 262350
 
6.0%
Dash Punctuation 97596
 
2.2%
Connector Punctuation 31324
 
0.7%
Math Symbol 29522
 
0.7%
Close Punctuation 17903
 
0.4%
Open Punctuation 17903
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 346616
11.8%
i 257257
 
8.7%
o 245368
 
8.3%
s 241797
 
8.2%
a 239086
 
8.1%
c 197005
 
6.7%
d 160752
 
5.5%
h 154074
 
5.2%
n 148896
 
5.1%
p 143175
 
4.9%
Other values (16) 809402
27.5%
Uppercase Letter
ValueCountFrequency (%)
C 34889
13.3%
I 26946
10.3%
D 23154
 
8.8%
A 22952
 
8.7%
V 21968
 
8.4%
O 16571
 
6.3%
P 14361
 
5.5%
N 14259
 
5.4%
S 13882
 
5.3%
T 11445
 
4.4%
Other values (16) 61923
23.6%
Decimal Number
ValueCountFrequency (%)
1 75649
25.4%
9 67253
22.6%
0 37079
12.5%
2 32343
10.9%
3 22466
 
7.6%
4 18917
 
6.4%
8 11904
 
4.0%
7 11547
 
3.9%
5 11409
 
3.8%
6 8778
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 339364
52.0%
. 195192
29.9%
: 86917
 
13.3%
& 23062
 
3.5%
? 6460
 
1.0%
% 750
 
0.1%
# 454
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 97596
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 31324
100.0%
Math Symbol
ValueCountFrequency (%)
= 29522
100.0%
Close Punctuation
ValueCountFrequency (%)
] 17903
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 17903
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3205778
73.7%
Common 1143792
 
26.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 346616
 
10.8%
i 257257
 
8.0%
o 245368
 
7.7%
s 241797
 
7.5%
a 239086
 
7.5%
c 197005
 
6.1%
d 160752
 
5.0%
h 154074
 
4.8%
n 148896
 
4.6%
p 143175
 
4.5%
Other values (42) 1071752
33.4%
Common
ValueCountFrequency (%)
/ 339364
29.7%
. 195192
17.1%
- 97596
 
8.5%
: 86917
 
7.6%
1 75649
 
6.6%
9 67253
 
5.9%
0 37079
 
3.2%
2 32343
 
2.8%
_ 31324
 
2.7%
= 29522
 
2.6%
Other values (12) 151553
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4349570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 346616
 
8.0%
/ 339364
 
7.8%
i 257257
 
5.9%
o 245368
 
5.6%
s 241797
 
5.6%
a 239086
 
5.5%
c 197005
 
4.5%
. 195192
 
4.5%
d 160752
 
3.7%
h 154074
 
3.5%
Other values (64) 1973059
45.4%

Interactions

2023-03-15T00:46:16.826757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:55.695843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:58.175574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:01.288483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:03.839156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:06.357446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:09.058337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:11.618478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:14.198602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:17.116635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:55.976551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:58.464516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:01.565932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:04.115800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:06.628103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:09.353528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:11.909291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:14.488841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:17.404521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:56.273829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:58.724824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:01.849383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:04.405513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:06.909571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:09.641804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:12.203568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:14.781742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:17.691551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:56.547431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:59.004422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:02.124183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:04.695727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:07.191797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:09.932684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:12.486351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:15.057511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:17.992904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:56.801462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:59.862673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:02.406395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:04.962629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:07.468333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:10.212611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:12.778376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:15.349705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:18.265812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:57.053719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:00.176464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:02.686412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:05.216620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:07.714586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:10.489778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:13.063815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:15.634623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:18.568028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:57.329083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:00.467654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:02.966955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:05.504628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:07.998777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:10.775454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:13.355553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:15.933568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:18.852773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:57.609806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:00.729594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:03.252971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:05.797067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:08.482662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:11.054903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:13.638478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:16.224787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:19.396574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:57.897572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:01.005282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:03.541752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:06.071720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:08.779580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:11.333772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:13.905679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:46:16.511128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-15T00:46:30.349224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
total_vaccinationspeople_vaccinatedpeople_fully_vaccinateddaily_vaccinations_rawdaily_vaccinationstotal_vaccinations_per_hundredpeople_vaccinated_per_hundredpeople_fully_vaccinated_per_hundreddaily_vaccinations_per_millionvaccinessource_name
total_vaccinations1.0000.9950.9840.7620.8400.5160.5200.4430.3340.3510.391
people_vaccinated0.9951.0000.9690.7980.8710.4510.4630.3720.3250.4490.481
people_fully_vaccinated0.9840.9691.0000.6880.7700.5250.5270.5220.2660.4190.457
daily_vaccinations_raw0.7620.7980.6881.0000.9400.0900.120-0.0000.4470.2970.300
daily_vaccinations0.8400.8710.7700.9401.0000.1270.1550.0350.4520.3010.306
total_vaccinations_per_hundred0.5160.4510.5250.0900.1271.0000.9870.9850.3520.2690.275
people_vaccinated_per_hundred0.5200.4630.5270.1200.1550.9871.0000.9730.3930.2950.393
people_fully_vaccinated_per_hundred0.4430.3720.522-0.0000.0350.9850.9731.0000.2120.2760.405
daily_vaccinations_per_million0.3340.3250.2660.4470.4520.3520.3930.2121.0000.0880.150
vaccines0.3510.4490.4190.2970.3010.2690.2950.2760.0881.0000.618
source_name0.3910.4810.4570.3000.3060.2750.3930.4050.1500.6181.000

Missing values

2023-03-15T00:46:19.944625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-15T00:46:20.843433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-15T00:46:21.849253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

countryiso_codedatetotal_vaccinationspeople_vaccinatedpeople_fully_vaccinateddaily_vaccinations_rawdaily_vaccinationstotal_vaccinations_per_hundredpeople_vaccinated_per_hundredpeople_fully_vaccinated_per_hundreddaily_vaccinations_per_millionvaccinessource_namesource_website
0AfghanistanAFG2021-02-220.00.0NaNNaNNaN0.000.00NaNNaNJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
1AfghanistanAFG2021-02-23NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
2AfghanistanAFG2021-02-24NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
3AfghanistanAFG2021-02-25NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
4AfghanistanAFG2021-02-26NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
5AfghanistanAFG2021-02-27NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
6AfghanistanAFG2021-02-288200.08200.0NaNNaN1367.00.020.02NaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
7AfghanistanAFG2021-03-01NaNNaNNaNNaN1580.0NaNNaNNaN40.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
8AfghanistanAFG2021-03-02NaNNaNNaNNaN1794.0NaNNaNNaN45.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
9AfghanistanAFG2021-03-03NaNNaNNaNNaN2008.0NaNNaNNaN50.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://covid19.who.int/
countryiso_codedatetotal_vaccinationspeople_vaccinatedpeople_fully_vaccinateddaily_vaccinations_rawdaily_vaccinationstotal_vaccinations_per_hundredpeople_vaccinated_per_hundredpeople_fully_vaccinated_per_hundreddaily_vaccinations_per_millionvaccinessource_namesource_website
86502ZimbabweZWE2022-03-208210637.04418956.03444793.02915.030641.054.4029.2822.832030.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
86503ZimbabweZWE2022-03-218230061.04432618.03446894.019424.09630.054.5329.3722.84638.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
86504ZimbabweZWE2022-03-228313471.04503937.03450864.083410.019990.055.0829.8422.871325.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
86505ZimbabweZWE2022-03-238414477.04589712.03455926.0101006.032456.055.7530.4122.902151.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
86506ZimbabweZWE2022-03-248552429.04704720.03461926.0137952.051151.056.6731.1722.943389.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
86507ZimbabweZWE2022-03-258691642.04814582.03473523.0139213.069579.057.5931.9023.024610.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
86508ZimbabweZWE2022-03-268791728.04886242.03487962.0100086.083429.058.2532.3823.115528.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
86509ZimbabweZWE2022-03-278845039.04918147.03493763.053311.090629.058.6132.5923.156005.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
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